Summary of Learning Unsupervised Semantic Document Representation For Fine-grained Aspect-based Sentiment Analysis, by Hao-ming Fu et al.
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis
by Hao-Ming Fu, Pu-Jen Cheng
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed unsupervised document representation learning model aims to overcome the weaknesses of existing sequential and non-sequential methods, which can be used for various natural language processing tasks. The model is evaluated on sentiment analysis (SA) datasets, achieving state-of-the-art performance on popular SA benchmarks and a fine-grained aspect-based SA by a significant margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to represent documents without using any labeled data. This representation can be used for many different tasks in natural language processing. The researchers tested their method on several sentiment analysis datasets and found that it performed better than other methods on these tasks. |
Keywords
* Artificial intelligence * Natural language processing * Representation learning * Unsupervised
